Instructions to use amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2")
model = AutoModelForCausalLM.from_pretrained("amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2

SGLang

How to use amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2 with Docker Model Runner:
```
docker model run hf.co/amalnuaimi/sqlcoder-7b-2-6.0bpw-exl2
```

Update notice

The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins.

If you downloaded the model before that, please redownload the weights for best performance.

Model Card for SQLCoder-7B-2

A capable large language model for natural language to SQL generation.

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Defog, Inc
Model type: [Text to SQL]
License: [CC-by-SA-4.0]
Finetuned from model: [CodeLlama-7B]

Model Sources [optional]

Uses

This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.

This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.

How to Get Started with the Model

Use the code here to get started with the model.

Prompt

Please use the following prompt for optimal results. Please remember to use do_sample=False and num_beams=4 for optimal results.

### Task
Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]

### Database Schema
The query will run on a database with the following schema:
{table_metadata_string_DDL_statements}

### Answer
Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]
[SQL]

Evaluation

This model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.

You can read more about the methodology behind SQLEval here.

Results

We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.

	date	group_by	order_by	ratio	join	where
sqlcoder-70b	96	91.4	97.1	85.7	97.1	91.4
sqlcoder-7b-2	96	91.4	94.3	91.4	94.3	77.1
sqlcoder-34b	80	94.3	85.7	77.1	85.7	80
gpt-4	72	94.3	97.1	80	91.4	80
gpt-4-turbo	76	91.4	91.4	62.8	88.6	77.1
natural-sql-7b	56	88.6	85.7	60	88.6	80
sqlcoder-7b	64	82.9	74.3	54.3	74.3	74.3
gpt-3.5	72	77.1	82.8	34.3	65.7	71.4
claude-2	52	71.4	74.3	57.1	65.7	62.9

Model Card Contact

Downloads last month: 2